The SagA of E faecium

An enzyme that remodels the cell wall of Enterococcus faecium helps these gut bacteria to divide and generate peptide fragments that enhance the immune response against cancer

New Insights About Immune Populations in Gastrointestinal GVHD

Jarosch et al.have deeply characterized immune cell infiltrates in gastrointestinal (GI) biopsies from individuals with GI graft-versus-host disease (GI-GvHD) using single-cell RNA sequencing and ChipCytometry. Individuals with severe GI-GvHD demonstrated increased clonally expanded cytotoxic CD8 T cells in GI biopsies

The Microbiome and Its Impact on Allogeneic Hematopoietic Cell Transplantation

Allogeneic hematopoietic cell transplantation (alloHCT) is a standard curative therapy for a variety of benign and malignant hematological diseases. Previously, patients who underwent alloHCT were at high risk for complications with potentially life-threatening toxicities, including a variety of opportunistic infections as well as acute and chronic manifestations of graft-versus-host disease (GVHD), where the transplanted immune system can produce inflammatory damage to the patient. With recent advances, including newer conditioning regimens, advances in viral and fungal infection prophylaxis, and novel GVHD prophylactic and treatment strategies, improvements in clinical outcomes have steadily improved. One modality with great potential that has yet to be fully realized is targeting the microbiome to further improve clinical outcomes.In recent years, the intestinal microbiota, which includes bacteria, fungi, viruses, and other microbes that reside within the intestinal tract, has become established as a potent modulator of alloHCT outcomes. The composition of intestinal bacteria, in particular, has been found in large multicenter prospective studies to be strongly associated with GVHD, treatment-related mortality, and overall survival. Murine studies have demonstrated a causal relationship between intestinal microbiota injury and aggravated GVHD, and more recently, clinical interventional studies of repleting the intestinal microbiota with fecal microbiota transplantation have emerged as effective therapies for GVHD. How the composition of the intestinal bacterial microbiota, which is often highly variable in alloHCT patients, can modulate GVHD and other outcomes is not fully understood. Recent studies, however, have begun to make substantial headway, including identifying particular bacterial subsets and/or bacterial-derived metabolites that can mediate harm or benefit. Here, the authors review recent studies that have improved our mechanistic understanding of the relationship between the microbiota and alloHCT outcomes, as well as studies that are beginning to establish strategies to modulate the microbiota with the hope of optimizing clinical outcomes

Dietary Fiber and Gut Bacteria Shape Infection Susceptibility

Gut microbiota composition has been reported to affect pathogen susceptibility but its specific effects, the underlying mechanisms and the potential influence of the diet remain unexplored. In their recent study, Desai and colleagues (Wolter et al, 2024), explore the complex interaction between diet, the gut microbiota and pathogen susceptibility, highlighting a diet-dependent role of the mucin-degrading microbe Akkermansia muciniphila

Microbiome Influencers of Checkpoint Blockade-Associated Toxicity

Immunotherapy has greatly improved cancer outcomes, yet variability in response and off-target tissue damage can occur with these treatments, including immune checkpoint inhibitors (ICIs). Multiple lines of evidence indicate the host microbiome influences ICI response and risk of immune-related adverse events (irAEs). As the microbiome is modifiable, these advances indicate the potential to manipulate microbiome components to increase ICI success. We discuss microbiome features associated with ICI response, with focus on bacterial taxa and potential immune mechanisms involved in irAEs, and the overall goal of driving novel approaches to manipulate the microbiome to improve ICI efficacy while avoiding irAE risk

survivalContour: Visualizing Predicted Survival via Colored Contour Plots

Advances in survival analysis have facilitated unprecedented flexibility in data modeling, yet there remains a lack of tools for illustrating the influence of continuous covariates on predicted survival outcomes. We propose the utilization of a colored contour plot to depict the predicted survival probabilities over time. Our approach is capable of supporting conventional models, including the Cox and Fine–Gray models. However, its capability shines when coupled with cutting-edge machine learning models such as random survival forests and deep neural networks. Availability and implementation We provide a Shiny app at https://biostatistics.mdanderson.org/shinyapps/survivalContour/ and an R package available at https://github.com/YushuShi/survivalContour as implementations of this tool

survivalContour: Visualizing predicted survival via colored contour plots

Advances in survival analysis have facilitated unprecedented flexibility in data modeling, yet there remains a lack of tools for graphically illustrating the influence of continuous covariates on predicted survival outcomes. We propose the utilization of a colored contour plot to depict the predicted survival probabilities over time, and provide a Shiny app and R package as implementations of this tool. Our approach is capable of supporting conventional models, including the Cox and Fine-Gray models. However, its capability shines when coupled with cutting-edge machine learning models such as random survival forests and deep neural networks

Taro: Tree-Aggregated Factor Regression for Microbiome Data Integration

MOTIVATION: Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns. RESULTS: We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the taxonomic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances. AVAILABILITY AND IMPLEMENTATION: The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package

Contribution of the Oral and Gastrointestinal Microbiomes to Bloodstream Infections in Leukemia Patients

Bloodstream infections (BSIs) pose a significant mortality risk for acute myeloid leukemia (AML) patients. It has been previously reported that intestinal domination (\u3e30% relative abundance [RA] attributed to a single taxon) with the infecting taxa often precedes BSI in stem cell transplant patients. Using 16S rRNA amplicon sequencing, we analyzed oral and stool samples from 63 AML patients with BSIs to determine the correlation between the infectious agent and microbiome composition. Whole-genome sequencing and antimicrobial susceptibilities were performed on all BSI isolates. Species-level detection of the infectious agent and presence of antibiotic resistance determinants in the stool